courses:ke
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| 两侧同时换到之前的修订记录前一修订版后一修订版 | 前一修订版 | ||
| courses:ke [2026/02/25 09:18] – [课程信息] whu | courses:ke [2026/04/07 14:00] (当前版本) – [课堂研讨] whu | ||
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| 将提供一批经典论文,并对这些论文提出一些导向性问题。学生需要自选阅读论文,结合对论文的提问,在课堂研讨中对论文的内容进行概述,并对论文中自己特别有体会的地方进行深入阐述。 | 将提供一批经典论文,并对这些论文提出一些导向性问题。学生需要自选阅读论文,结合对论文的提问,在课堂研讨中对论文的内容进行概述,并对论文中自己特别有体会的地方进行深入阐述。 | ||
| - | **论文挑选规则.** 两人一组,根据报告人学号尾数按 mod 3 的余数选择论文主题;每个主题内的论文可以自由选择;每组报告时长为 | + | **论文挑选规则.** 两人一组,根据报告人学号尾数按 mod 3 的余数选择论文主题;每个主题内的论文可以自由选择;每组报告时长为 |
| - | * **第一次课堂研讨的论文: | + | * **第一次课堂研讨的论文: |
| * 知识抽取 (// | * 知识抽取 (// | ||
| * Improving Distantly-Supervised Relation Extraction with Joint Label Embedding | * Improving Distantly-Supervised Relation Extraction with Joint Label Embedding | ||
| 行 57: | 行 57: | ||
| * Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion | * Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion | ||
| * Probase: A Probabilistic Taxonomy for Text Understanding | * Probase: A Probabilistic Taxonomy for Text Understanding | ||
| - | * 预训练语言模型 (// | + | * 语言模型知识增强 |
| - | * K-BERT: Enabling | + | * KEPLER: A Unified Model for Knowledge Embedding and Pre-trained |
| - | * ERNIE: Enhanced Language | + | * ERNIE 3.0: Large-scale Knowledge |
| - | * KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation | + | * WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization |
| - | * **第二次课堂研讨的论文: | + | * **第二次课堂研讨的论文: |
| * 搜索推荐 (// | * 搜索推荐 (// | ||
| - | * Fielded Sequential Dependence Model for Ad-Hoc Entity Retrieval in the Web of Data | + | * |
| - | * What Links Alice and Bob?: Matching and Ranking Semantic Patterns in Heterogeneous Networks | + | |
| - | * RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems | + | |
| * 知识融合 (// | * 知识融合 (// | ||
| - | * PARIS: Probabilistic Alignment of Relations, Instances, and Schema | + | * |
| - | * Deep Learning for Entity Matching: A Design Space Exploration | + | |
| - | * Deep Entity Matching with Pre-Trained Language Models | + | |
| * 机器问答 (// | * 机器问答 (// | ||
| - | * Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs | + | * |
| - | * Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings | + | |
| - | * RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering | + | |
| ---- | ---- | ||
| 行 92: | 行 86: | ||
| <WRAP half column> | <WRAP half column> | ||
| 教师:[[whu@nju.edu.cn|胡伟]]\\ | 教师:[[whu@nju.edu.cn|胡伟]]\\ | ||
| - | 答疑时间:周五下午 2~4 点 | + | 答疑时间:周二下午 2~4 点 |
| </ | </ | ||
| <WRAP half column> | <WRAP half column> | ||
| - | QQ群:1034965957 | + | QQ群:327767069 |
| 地点:仙林校区计算机楼 405 室 | 地点:仙林校区计算机楼 405 室 | ||
| </ | </ | ||
| 行 109: | 行 103: | ||
| * 肖仰华 等. 知识图谱: | * 肖仰华 等. 知识图谱: | ||
| * 王昊奋, 漆桂林, 陈华钧. 知识图谱: | * 王昊奋, 漆桂林, 陈华钧. 知识图谱: | ||
| + | * 赵鑫, 李军毅, 周昆, 文继荣. 大语言模型. 2024 | ||
courses/ke.1771982307.txt.gz · 最后更改: 2026/02/25 09:18 由 whu